{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from __future__ import print_function\n",
    "import collections\n",
    "import math\n",
    "import numpy as np\n",
    "import os\n",
    "import random\n",
    "import tensorflow as tf\n",
    "import zipfile\n",
    "from matplotlib import pylab\n",
    "from six.moves import range\n",
    "from six.moves.urllib.request import urlretrieve\n",
    "from sklearn.manifold import TSNE\n",
    "from tempfile import gettempdir\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "file = open('QuanSongCi.txt',encoding=\"utf8\")\n",
    "dataTotal = file.read()\n",
    "chars = list(set(dataTotal))\n",
    "data_size, num_classes = len(dataTotal), len(chars)\n",
    "words = chars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Most common words (+UNK) [['UNK', 1011], ('搊', 1), ('坚', 1), ('彷', 1), ('谪', 1)]\n",
      "Sample data [1, 2, 3, 17, 4, 7, 9, 12, 14, 19]\n"
     ]
    }
   ],
   "source": [
    "vocabulary_size = 5000\n",
    "\n",
    "def build_dataset(words):\n",
    "  count = [['UNK', -1]]\n",
    "  count.extend(collections.Counter(words).most_common(vocabulary_size - 1))\n",
    "  dictionary = dict()\n",
    "  for word, _ in count:\n",
    "    dictionary[word] = len(dictionary)\n",
    "  data = list()\n",
    "  unk_count = 0\n",
    "  for word in words:\n",
    "    if word in dictionary:\n",
    "      index = dictionary[word]\n",
    "    else:\n",
    "      index = 0  # dictionary['UNK']\n",
    "      unk_count = unk_count + 1\n",
    "    data.append(index)\n",
    "  count[0][1] = unk_count\n",
    "  reverse_dictionary = dict(zip(dictionary.values(), dictionary.keys())) \n",
    "  return data, count, dictionary, reverse_dictionary\n",
    "\n",
    "data, count, dictionary, reverse_dictionary = build_dataset(words)\n",
    "print('Most common words (+UNK)', count[:5])\n",
    "print('Sample data', data[:10])\n",
    "del words  # Hint to reduce memory."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data: ['搊', '坚', '彷', '駈', '谪', '骍', '屼', '咻']\n",
      "\n",
      "with num_skips = 2 and skip_window = 1:\n",
      "    batch: ['坚', '坚', '彷', '彷', '駈', '駈', '谪', '谪']\n",
      "    labels: ['彷', '搊', '坚', '駈', '彷', '谪', '骍', '駈']\n",
      "\n",
      "with num_skips = 4 and skip_window = 2:\n",
      "    batch: ['彷', '彷', '彷', '彷', '駈', '駈', '駈', '駈']\n",
      "    labels: ['谪', '駈', '坚', '搊', '彷', '谪', '坚', '骍']\n"
     ]
    }
   ],
   "source": [
    "\n",
    "data_index = 0\n",
    "\n",
    "def generate_batch(batch_size, num_skips, skip_window):\n",
    "  global data_index\n",
    "  assert batch_size % num_skips == 0\n",
    "  assert num_skips <= 2 * skip_window\n",
    "  batch = np.ndarray(shape=(batch_size), dtype=np.int32)\n",
    "  labels = np.ndarray(shape=(batch_size, 1), dtype=np.int32)\n",
    "  span = 2 * skip_window + 1 # [ skip_window target skip_window ]\n",
    "  buffer = collections.deque(maxlen=span)\n",
    "  for _ in range(span):\n",
    "    buffer.append(data[data_index])\n",
    "    data_index = (data_index + 1) % len(data)\n",
    "  for i in range(batch_size // num_skips):\n",
    "    target = skip_window  # target label at the center of the buffer\n",
    "    targets_to_avoid = [ skip_window ]\n",
    "    for j in range(num_skips):\n",
    "      while target in targets_to_avoid:\n",
    "        target = random.randint(0, span - 1)\n",
    "      targets_to_avoid.append(target)\n",
    "      batch[i * num_skips + j] = buffer[skip_window]\n",
    "      labels[i * num_skips + j, 0] = buffer[target]\n",
    "    buffer.append(data[data_index])\n",
    "    data_index = (data_index + 1) % len(data)\n",
    "  return batch, labels\n",
    "\n",
    "print('data:', [reverse_dictionary[di] for di in data[:8]])\n",
    "\n",
    "for num_skips, skip_window in [(2, 1), (4, 2)]:\n",
    "    data_index = 0\n",
    "    batch, labels = generate_batch(batch_size=8, num_skips=num_skips, skip_window=skip_window)\n",
    "    print('\\nwith num_skips = %d and skip_window = %d:' % (num_skips, skip_window))\n",
    "    print('    batch:', [reverse_dictionary[bi] for bi in batch])\n",
    "    print('    labels:', [reverse_dictionary[li] for li in labels.reshape(8)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 128\n",
    "embedding_size = 128 # Dimension of the embedding vector.\n",
    "skip_window = 1 # How many words to consider left and right.\n",
    "num_skips = 2 # How many times to reuse an input to generate a label.\n",
    "# We pick a random validation set to sample nearest neighbors. here we limit the\n",
    "# validation samples to the words that have a low numeric ID, which by\n",
    "# construction are also the most frequent. \n",
    "valid_size = 16 # Random set of words to evaluate similarity on.\n",
    "valid_window = 100 # Only pick dev samples in the head of the distribution.\n",
    "valid_examples = np.array(random.sample(range(valid_window), valid_size))\n",
    "num_sampled = 64 # Number of negative examples to sample.\n",
    "\n",
    "graph = tf.Graph()\n",
    "\n",
    "with graph.as_default(), tf.device('/cpu:0'):\n",
    "\n",
    "  # Input data.\n",
    "  train_dataset = tf.placeholder(tf.int32, shape=[batch_size])\n",
    "  train_labels = tf.placeholder(tf.int32, shape=[batch_size, 1])\n",
    "  valid_dataset = tf.constant(valid_examples, dtype=tf.int32)\n",
    "  \n",
    "  # Variables.\n",
    "  embeddings = tf.Variable(\n",
    "    tf.random_uniform([vocabulary_size, embedding_size], -1.0, 1.0))\n",
    "  softmax_weights = tf.Variable(\n",
    "    tf.truncated_normal([vocabulary_size, embedding_size],\n",
    "                         stddev=1.0 / math.sqrt(embedding_size)))\n",
    "  softmax_biases = tf.Variable(tf.zeros([vocabulary_size]))\n",
    "  \n",
    "  # Model.\n",
    "  # Look up embeddings for inputs.\n",
    "  embed = tf.nn.embedding_lookup(embeddings, train_dataset)\n",
    "  # Compute the softmax loss, using a sample of the negative labels each time.\n",
    "  loss = tf.reduce_mean(\n",
    "    tf.nn.nce_loss(softmax_weights, softmax_biases, train_labels,embed, num_sampled, vocabulary_size))\n",
    "\n",
    "  # Optimizer.\n",
    "  optimizer = tf.train.AdagradOptimizer(1.0).minimize(loss)\n",
    "  \n",
    "  # Compute the similarity between minibatch examples and all embeddings.\n",
    "  # We use the cosine distance:\n",
    "  norm = tf.sqrt(tf.reduce_sum(tf.square(embeddings), 1, keep_dims=True))\n",
    "  normalized_embeddings = embeddings / norm\n",
    "  valid_embeddings = tf.nn.embedding_lookup(\n",
    "    normalized_embeddings, valid_dataset)\n",
    "  similarity = tf.matmul(valid_embeddings, tf.transpose(normalized_embeddings))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From C:\\Anaconda\\envs\\python3\\lib\\site-packages\\tensorflow\\python\\util\\tf_should_use.py:107: initialize_all_variables (from tensorflow.python.ops.variables) is deprecated and will be removed after 2017-03-02.\n",
      "Instructions for updating:\n",
      "Use `tf.global_variables_initializer` instead.\n",
      "Initialized\n",
      "Average loss at step 0: 178.607941\n",
      "Nearest to 堤: 璋, 蠙, 谣, 移, 噪, 窦, 骆, 儡,\n",
      "Nearest to 谨: 法, 淦, , 鴂, 发, 籧, 勃, 粱,\n",
      "Nearest to 谛: 翎, 儿, , 萦, 瘥, 邙, 助, 聃,\n",
      "Nearest to 渌: 鄘, 浩, 攥, 坂, 抡, 伟, 夏, 脂,\n",
      "Nearest to 筒: 疲, 纵, 到, 颙, 改, 赪, 篰, 怳,\n",
      "Nearest to 骍: 者, 滪, 习, 篰, 共, 货, 乇, 氲,\n",
      "Nearest to 颣: 厩, 跣, 圈, 尖, 摩, 涕, 眼, 鹆,\n",
      "Nearest to 蜘: 搴, 咎, 簟, 祲, 彳, 曲, 幕, 敬,\n",
      "Nearest to 呕: 般, 甓, 秾, 店, , 莫, 僻, 笙,\n",
      "Nearest to 泖: 灸, 邵, 堍, 诸, 杨, 虔, 滞, 蓬,\n",
      "Nearest to 邱: 陷, 境, 钳, 召, 嗷, 皴, 咸, 匣,\n",
      "Nearest to 贰: 躞, 裉, 鵕, 浔, 粜, 趟, 仟, 艮,\n",
      "Nearest to 羊: 雏, 帽, 饱, 蜍, 虱, 俳, 眚, 沍,\n",
      "Nearest to 牥: 侈, 掖, 肠, 扬, 旻, 鞮, 浚, 纟,\n",
      "Nearest to 嶓: 但, 鬖, 皋, 舷, 箬, 谳, 么, 酌,\n",
      "Nearest to 愧: , 尾, 钝, 盥, 忏, 軿, 傀, 蹒,\n",
      "Average loss at step 2000: 19.504706\n",
      "Average loss at step 4000: 0.858551\n",
      "Average loss at step 6000: 0.667425\n",
      "Average loss at step 8000: 0.642524\n",
      "Average loss at step 10000: 0.617772\n",
      "Nearest to 堤: 璋, 窦, 骆, 揭, 谣, 蠙, 瘥, 蛾,\n",
      "Nearest to 谨: 籧, 外, 法, 捧, 嘘, 代, 披, 中,\n",
      "Nearest to 谛: 翎, 萦, 儿, , 屠, 蕊, 鞯, 聃,\n",
      "Nearest to 渌: 鄘, 攥, 疾, 哩, 谠, 抡, 濠, 祝,\n",
      "Nearest to 筒: 疲, 纵, 萤, 焰, 到, 赪, 篰, 磅,\n",
      "Nearest to 骍: 滪, 者, 乇, 篰, 棣, 习, 共, 帨,\n",
      "Nearest to 颣: 尖, 厩, 圈, 莠, 摩, 跣, 娣, 口,\n",
      "Nearest to 蜘: 簟, 彳, 祲, 幕, 蕾, 沚, 咎, 噪,\n",
      "Nearest to 呕: 甓, 般, 莫, 囿, 狮, 店, 仂, 秾,\n",
      "Nearest to 泖: 邵, 灸, 诸, 鹜, 蓬, 堍, 奴, 眇,\n",
      "Nearest to 邱: 陷, 嗷, 钳, 烧, 匣, 咸, , 皴,\n",
      "Nearest to 贰: 躞, 裉, 趟, 亩, 挥, 仟, 不, 诫,\n",
      "Nearest to 羊: 雏, 饱, 帽, 眚, 沍, 虱, 恻, 蜍,\n",
      "Nearest to 牥: 侈, 瓃, 纟, 掖, 旻, 扬, 偿, 饭,\n",
      "Nearest to 嶓: 鬖, 但, 箬, 皋, 谳, 衾, 舷, ④,\n",
      "Nearest to 愧: 軿, , 敌, 蹒, 溅, 傀, 爆, 黏,\n",
      "Average loss at step 12000: 0.606738\n",
      "Average loss at step 14000: 0.604248\n",
      "Average loss at step 16000: 0.589922\n",
      "Average loss at step 18000: 0.596097\n",
      "Average loss at step 20000: 0.584676\n",
      "Nearest to 堤: 璋, 窦, 揭, 骆, 谣, 瘥, 蛾, 儡,\n",
      "Nearest to 谨: 籧, 外, 法, 捧, 嘘, 代, 披, 噪,\n",
      "Nearest to 谛: 翎, 萦, , 屠, 儿, 鞯, 蜜, 蕊,\n",
      "Nearest to 渌: 鄘, 攥, 疾, 哩, 谠, 濠, 抡, 祝,\n",
      "Nearest to 筒: 疲, 纵, 焰, 萤, 到, 篰, 磅, 赪,\n",
      "Nearest to 骍: 滪, 者, 乇, 篰, 习, 棣, 共, 帨,\n",
      "Nearest to 颣: 尖, 厩, 圈, 摩, 莠, 跣, 娣, 口,\n",
      "Nearest to 蜘: 簟, 彳, 祲, 幕, 蕾, 尿, 沚, 迸,\n",
      "Nearest to 呕: 甓, 般, 莫, 狮, 仂, 秾, 店, 囿,\n",
      "Nearest to 泖: 邵, 灸, 诸, 鹜, 蓬, 奴, 堍, 眇,\n",
      "Nearest to 邱: 陷, 嗷, 钳, 匣, 烧, 咸, , 序,\n",
      "Nearest to 贰: 躞, 裉, 趟, 亩, 挥, 仟, 不, 诫,\n",
      "Nearest to 羊: 雏, 饱, 帽, 眚, 虱, 沍, 恻, 墓,\n",
      "Nearest to 牥: 侈, 纟, 瓃, 掖, 饭, 偿, 旻, 艨,\n",
      "Nearest to 嶓: 鬖, 但, 箬, 皋, 衾, 谳, ④, 舷,\n",
      "Nearest to 愧: 軿, , 敌, 蹒, 溅, 傀, 远, 爆,\n",
      "Average loss at step 22000: 0.580519\n",
      "Average loss at step 24000: 0.586084\n",
      "Average loss at step 26000: 0.576124\n",
      "Average loss at step 28000: 0.570872\n",
      "Average loss at step 30000: 0.576212\n",
      "Nearest to 堤: 窦, 璋, 揭, 骆, 谣, 瘥, 駣, 蛾,\n",
      "Nearest to 谨: 籧, 外, 法, 捧, 嘘, 代, 披, 噪,\n",
      "Nearest to 谛: 翎, 萦, , 屠, 儿, 鞯, 蕊, 悒,\n",
      "Nearest to 渌: 鄘, 攥, 疾, 哩, 濠, 祝, 抡, 脂,\n",
      "Nearest to 筒: 疲, 纵, 焰, 萤, 到, 磅, 篰, 赪,\n",
      "Nearest to 骍: 者, 滪, 乇, 篰, 习, 棣, 帨, 共,\n",
      "Nearest to 颣: 尖, 厩, 圈, 莠, 摩, 跣, 娣, 口,\n",
      "Nearest to 蜘: 簟, 祲, 彳, 幕, 蕾, 尿, 咎, 蹴,\n",
      "Nearest to 呕: 甓, 般, 狮, 莫, 秾, 店, 囿, 仂,\n",
      "Nearest to 泖: 邵, 灸, 诸, 鹜, 蓬, 堍, 奴, 眇,\n",
      "Nearest to 邱: 陷, 嗷, 钳, 咸, 匣, 烧, , 序,\n",
      "Nearest to 贰: 躞, 裉, 趟, 亩, 挥, 仟, 不, 诫,\n",
      "Nearest to 羊: 雏, 饱, 帽, 眚, 恻, 沍, 墓, 虱,\n",
      "Nearest to 牥: 侈, 纟, 瓃, 偿, 饭, 掖, 旻, 扬,\n",
      "Nearest to 嶓: 鬖, 但, 箬, 皋, 衾, 谳, ④, 舷,\n",
      "Nearest to 愧: 軿, , 敌, 蹒, 溅, 傀, 爆, 靸,\n",
      "Average loss at step 32000: 0.567410\n",
      "Average loss at step 34000: 0.574200\n",
      "Average loss at step 36000: 0.571163\n",
      "Average loss at step 38000: 0.575111\n",
      "Average loss at step 40000: 0.564244\n",
      "Nearest to 堤: 窦, 璋, 揭, 谣, 骆, 瘥, 蛾, 儡,\n",
      "Nearest to 谨: 籧, 外, 法, 捧, 嘘, 代, 披, 中,\n",
      "Nearest to 谛: 翎, 萦, , 屠, 儿, 鞯, 拄, 瘥,\n",
      "Nearest to 渌: 鄘, 攥, 疾, 哩, 濠, 抡, 祝, 脂,\n",
      "Nearest to 筒: 疲, 纵, 焰, 萤, 到, 篰, 磅, 赪,\n",
      "Nearest to 骍: 者, 滪, 乇, 篰, 习, 棣, 货, 帨,\n",
      "Nearest to 颣: 尖, 厩, 摩, 圈, 莠, 娣, 醲, 跣,\n",
      "Nearest to 蜘: 簟, 彳, 祲, 幕, 蕾, 蹴, 刃, 玎,\n",
      "Nearest to 呕: 般, 甓, 狮, 莫, 秾, 囿, 店, 僻,\n",
      "Nearest to 泖: 邵, 灸, 诸, 蓬, 鹜, 堍, 眇, 奴,\n",
      "Nearest to 邱: 陷, 嗷, 钳, 匣, 咸, 烧, , 芰,\n",
      "Nearest to 贰: 躞, 趟, 裉, 亩, 挥, 仟, 不, 诫,\n",
      "Nearest to 羊: 帽, 雏, 饱, 恻, 眚, 沍, 墓, 虱,\n",
      "Nearest to 牥: 侈, 纟, 瓃, 掖, 旻, 饭, 偿, 扬,\n",
      "Nearest to 嶓: 鬖, 但, 皋, 箬, 衾, 谳, ④, 舷,\n",
      "Nearest to 愧: 軿, , 敌, 蹒, 傀, 溅, 爆, 黏,\n",
      "Average loss at step 42000: 0.564267\n",
      "Average loss at step 44000: 0.562861\n",
      "Average loss at step 46000: 0.562043\n",
      "Average loss at step 48000: 0.569685\n",
      "Average loss at step 50000: 0.562786\n",
      "Nearest to 堤: 窦, 璋, 谣, 揭, 骆, 瘥, 儡, 蛾,\n",
      "Nearest to 谨: 籧, 外, 法, 捧, 嘘, 代, 披, 中,\n",
      "Nearest to 谛: 翎, 萦, , 屠, 儿, 瘥, 胡, 鞯,\n",
      "Nearest to 渌: 鄘, 攥, 疾, 哩, 濠, 祝, 脂, 抡,\n",
      "Nearest to 筒: 疲, 纵, 焰, 萤, 到, 篰, 磅, 赪,\n",
      "Nearest to 骍: 者, 滪, 乇, 习, 篰, 棣, 帨, 共,\n",
      "Nearest to 颣: 厩, 尖, 圈, 莠, 摩, 跣, 娣, 辽,\n",
      "Nearest to 蜘: 簟, 彳, 幕, 祲, 蕾, 刃, 咎, 戊,\n",
      "Nearest to 呕: 甓, 般, 莫, 狮, 囿, 僻, 秾, 店,\n",
      "Nearest to 泖: 邵, 灸, 诸, 鹜, 蓬, 奴, 堍, 眇,\n",
      "Nearest to 邱: 陷, 嗷, 匣, 钳, 烧, 咸, 芰, ,\n",
      "Nearest to 贰: 躞, 趟, 裉, 亩, 挥, 不, 仟, 诫,\n",
      "Nearest to 羊: 帽, 饱, 雏, 眚, 恻, 墓, 虱, 沍,\n",
      "Nearest to 牥: 侈, 瓃, 纟, 旻, 掖, 饭, 偿, 扬,\n",
      "Nearest to 嶓: 鬖, 但, 皋, 衾, 箬, 谳, ④, 舷,\n",
      "Nearest to 愧: 軿, , 敌, 蹒, 傀, 爆, 溅, 黏,\n",
      "Average loss at step 52000: 0.561223\n",
      "Average loss at step 54000: 0.564114\n",
      "Average loss at step 56000: 0.562761\n",
      "Average loss at step 58000: 0.565027\n",
      "Average loss at step 60000: 0.559290\n",
      "Nearest to 堤: 窦, 璋, 瘥, 谣, 揭, 骆, 儡, 蛾,\n",
      "Nearest to 谨: 籧, 外, 法, 捧, 嘘, 代, 噪, 潸,\n",
      "Nearest to 谛: 翎, 萦, 屠, , 儿, 拄, 瘥, 鞯,\n",
      "Nearest to 渌: 鄘, 攥, 疾, 哩, 濠, 祝, 妹, 抡,\n",
      "Nearest to 筒: 疲, 纵, 焰, 萤, 到, 磅, 篰, 赪,\n",
      "Nearest to 骍: 者, 习, 乇, 滪, 篰, 棣, 共, 帨,\n",
      "Nearest to 颣: 厩, 尖, 圈, 摩, 莠, 跣, 娣, 醲,\n",
      "Nearest to 蜘: 簟, 彳, 祲, 蕾, 幕, 刃, 戊, 迸,\n",
      "Nearest to 呕: 甓, 般, 莫, 狮, 囿, 店, 秾, 仂,\n",
      "Nearest to 泖: 邵, 灸, 诸, 鹜, 蓬, 堍, 眇, 奴,\n",
      "Nearest to 邱: 陷, 嗷, 钳, 匣, 咸, 烧, 序, 飘,\n",
      "Nearest to 贰: 躞, 裉, 趟, 亩, 挥, 不, 仟, 诫,\n",
      "Nearest to 羊: 帽, 饱, 雏, 眚, 恻, 沍, 墓, 擢,\n",
      "Nearest to 牥: 侈, 纟, 瓃, 旻, 掖, 饭, 浚, 偿,\n",
      "Nearest to 嶓: 鬖, 但, 皋, 箬, 衾, 谳, ④, 郿,\n",
      "Nearest to 愧: 軿, , 敌, 蹒, 傀, 爆, 溅, 黏,\n",
      "Average loss at step 62000: 0.563847\n",
      "Average loss at step 64000: 0.563672\n",
      "Average loss at step 66000: 0.554420\n",
      "Average loss at step 68000: 0.563070\n",
      "Average loss at step 70000: 0.554389\n",
      "Nearest to 堤: 窦, 璋, 揭, 骆, 谣, 瘥, 駣, 儡,\n",
      "Nearest to 谨: 籧, 外, 法, 捧, 嘘, 代, 披, 噪,\n",
      "Nearest to 谛: 翎, 萦, 儿, 屠, , 悒, 瘥, 拄,\n",
      "Nearest to 渌: 鄘, 攥, 疾, 濠, 哩, 祝, 脂, 妹,\n",
      "Nearest to 筒: 疲, 纵, 焰, 萤, 到, 磅, 篰, 赪,\n",
      "Nearest to 骍: 者, 滪, 习, 乇, 棣, 篰, 共, 帨,\n",
      "Nearest to 颣: 尖, 厩, 圈, 莠, 摩, 跣, 口, 醲,\n",
      "Nearest to 蜘: 簟, 祲, 彳, 幕, 蕾, 蹴, 戊, 刃,\n",
      "Nearest to 呕: 甓, 般, 莫, 狮, 囿, 秾, 僻, 店,\n",
      "Nearest to 泖: 邵, 灸, 诸, 蓬, 奴, 鹜, 眇, 堍,\n",
      "Nearest to 邱: 陷, 嗷, 钳, 匣, 芰, 咸, 立, 烧,\n",
      "Nearest to 贰: 躞, 裉, 趟, 挥, 亩, 仟, 诫, 不,\n",
      "Nearest to 羊: 帽, 雏, 饱, 眚, 恻, 擢, 墓, 虱,\n",
      "Nearest to 牥: 侈, 瓃, 纟, 旻, 饭, 掖, 扬, 浚,\n",
      "Nearest to 嶓: 鬖, 但, 皋, 箬, 衾, 谳, ④, 郿,\n",
      "Nearest to 愧: 軿, , 敌, 蹒, 傀, 黏, 忏, 溅,\n",
      "Average loss at step 72000: 0.563025\n",
      "Average loss at step 74000: 0.555945\n",
      "Average loss at step 76000: 0.560498\n",
      "Average loss at step 78000: 0.563989\n",
      "Average loss at step 80000: 0.547903\n",
      "Nearest to 堤: 窦, 璋, 谣, 揭, 骆, 瘥, 儡, 蛾,\n",
      "Nearest to 谨: 籧, 外, 法, 捧, 嘘, 代, 噪, 潸,\n",
      "Nearest to 谛: 翎, 萦, 屠, , 儿, 拄, 胡, 鞯,\n",
      "Nearest to 渌: 鄘, 攥, 疾, 哩, 祝, 濠, 脂, 妹,\n",
      "Nearest to 筒: 疲, 焰, 纵, 萤, 到, 磅, 篰, 赪,\n",
      "Nearest to 骍: 者, 习, 乇, 滪, 棣, 篰, 共, 帨,\n",
      "Nearest to 颣: 尖, 厩, 圈, 摩, 莠, 娣, 扰, 醲,\n",
      "Nearest to 蜘: 簟, 彳, 祲, 幕, 蕾, 蹴, 戊, 咎,\n",
      "Nearest to 呕: 甓, 般, 莫, 狮, 囿, 僻, 秾, 仂,\n",
      "Nearest to 泖: 邵, 灸, 诸, 蓬, 眇, 鹜, 奴, 堍,\n",
      "Nearest to 邱: 陷, 嗷, 匣, 钳, , 咸, 芰, 烧,\n",
      "Nearest to 贰: 躞, 趟, 挥, 裉, 亩, 不, 仟, 诫,\n",
      "Nearest to 羊: 帽, 雏, 饱, 眚, 恻, 沍, 墓, 擢,\n",
      "Nearest to 牥: 侈, 纟, 瓃, 掖, 旻, 饭, 偿, 扬,\n",
      "Nearest to 嶓: 鬖, 但, 箬, 皋, 衾, 谳, ④, 郿,\n",
      "Nearest to 愧: 軿, , 敌, 蹒, 傀, 黏, 溅, 陷,\n",
      "Average loss at step 82000: 0.560350\n",
      "Average loss at step 84000: 0.555990\n",
      "Average loss at step 86000: 0.553130\n",
      "Average loss at step 88000: 0.556040\n",
      "Average loss at step 90000: 0.555195\n",
      "Nearest to 堤: 窦, 璋, 揭, 谣, 儡, 瘥, 骆, 駣,\n",
      "Nearest to 谨: 籧, 外, 法, 嘘, 捧, 代, 噪, 中,\n",
      "Nearest to 谛: 翎, 萦, 屠, , 儿, 拄, 鞯, 馌,\n",
      "Nearest to 渌: 鄘, 攥, 疾, 濠, 妹, 祝, 哩, 抡,\n",
      "Nearest to 筒: 疲, 纵, 焰, 萤, 到, 篰, 磅, 蔷,\n",
      "Nearest to 骍: 者, 习, 乇, 篰, 滪, 棣, 共, 帨,\n",
      "Nearest to 颣: 厩, 尖, 圈, 摩, 莠, 口, 跣, 醲,\n",
      "Nearest to 蜘: 簟, 彳, 祲, 蕾, 幕, 蹴, 戊, 刃,\n",
      "Nearest to 呕: 甓, 般, 莫, 囿, 狮, 淄, 店, 僻,\n",
      "Nearest to 泖: 邵, 灸, 诸, 眇, 鹜, 蓬, 堍, 奴,\n",
      "Nearest to 邱: 陷, 嗷, 匣, 芰, 钳, 飘, 烧, 咸,\n",
      "Nearest to 贰: 躞, 趟, 裉, 挥, 亩, 仟, 不, 诫,\n",
      "Nearest to 羊: 帽, 饱, 雏, 眚, 恻, 墓, 擢, 沍,\n",
      "Nearest to 牥: 侈, 纟, 瓃, 掖, 饭, 旻, 偿, 7,\n",
      "Nearest to 嶓: 鬖, 但, 箬, 皋, 衾, 谳, ④, 郿,\n",
      "Nearest to 愧: 軿, , 敌, 蹒, 傀, 黏, 远, 爆,\n",
      "Average loss at step 92000: 0.556908\n",
      "Average loss at step 94000: 0.555526\n",
      "Average loss at step 96000: 0.559157\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average loss at step 98000: 0.553185\n",
      "Average loss at step 100000: 0.547153\n",
      "Nearest to 堤: 窦, 璋, 揭, 谣, 瘥, 儡, 駣, 骆,\n",
      "Nearest to 谨: 外, 籧, 法, 捧, 嘘, 代, 噪, 中,\n",
      "Nearest to 谛: 翎, 萦, 屠, , 儿, 拄, 馌, 胡,\n",
      "Nearest to 渌: 鄘, 攥, 疾, 哩, 濠, 妹, 祝, 抡,\n",
      "Nearest to 筒: 疲, 焰, 纵, 萤, 到, 磅, 蔷, 篰,\n",
      "Nearest to 骍: 者, 乇, 习, 篰, 滪, 棣, 共, 帨,\n",
      "Nearest to 颣: 厩, 尖, 圈, 摩, 莠, 辽, 娣, 跣,\n",
      "Nearest to 蜘: 簟, 彳, 蕾, 祲, 幕, 戊, 刃, 咎,\n",
      "Nearest to 呕: 甓, 般, 莫, 狮, 囿, 秾, 僻, 店,\n",
      "Nearest to 泖: 邵, 灸, 诸, 蓬, 奴, 鹜, 眇, 堍,\n",
      "Nearest to 邱: 陷, 嗷, 匣, 钳, 芰, , 咸, 烧,\n",
      "Nearest to 贰: 躞, 趟, 裉, 亩, 挥, 不, 仟, 鵕,\n",
      "Nearest to 羊: 帽, 饱, 雏, 眚, 恻, 墓, 擢, 沍,\n",
      "Nearest to 牥: 侈, 瓃, 纟, 旻, 掖, 饭, 偿, 浚,\n",
      "Nearest to 嶓: 鬖, 但, 箬, 皋, 衾, 谳, ④, 舷,\n",
      "Nearest to 愧: 軿, , 敌, 蹒, 傀, 黏, 溅, 矧,\n",
      "Average loss at step 102000: 0.557699\n",
      "Average loss at step 104000: 0.547414\n",
      "Average loss at step 106000: 0.553490\n",
      "Average loss at step 108000: 0.562340\n",
      "Average loss at step 110000: 0.555089\n",
      "Nearest to 堤: 窦, 璋, 谣, 揭, 瘥, 儡, 骆, 蛾,\n",
      "Nearest to 谨: 籧, 外, 法, 捧, 嘘, 代, 噪, 潸,\n",
      "Nearest to 谛: 翎, 萦, 屠, 儿, , 拄, 厌, 鹥,\n",
      "Nearest to 渌: 鄘, 攥, 疾, 哩, 濠, 祝, 妹, 抡,\n",
      "Nearest to 筒: 疲, 纵, 萤, 焰, 到, 篰, 磅, 哺,\n",
      "Nearest to 骍: 者, 棣, 滪, 篰, 习, 乇, 茕, 聚,\n",
      "Nearest to 颣: 厩, 尖, 摩, 圈, 莠, 娣, 口, 醲,\n",
      "Nearest to 蜘: 簟, 彳, 蕾, 幕, 祲, 迸, 刃, 戊,\n",
      "Nearest to 呕: 甓, 般, 莫, 僻, 狮, 囿, 秾, 淄,\n",
      "Nearest to 泖: 邵, 灸, 诸, 奴, 蓬, 眇, 鹜, 堍,\n",
      "Nearest to 邱: 陷, 嗷, 钳, 匣, 烧, 芰, 飘, 立,\n",
      "Nearest to 贰: 躞, 趟, 裉, 挥, 亩, 诫, 不, 仟,\n",
      "Nearest to 羊: 帽, 饱, 雏, 眚, 恻, 墓, 沍, 擢,\n",
      "Nearest to 牥: 侈, 纟, 瓃, 旻, 饭, 掖, 偿, 7,\n",
      "Nearest to 嶓: 鬖, 但, 箬, 皋, 衾, ④, 谳, 郿,\n",
      "Nearest to 愧: 軿, , 敌, 蹒, 傀, 黏, 矧, 远,\n",
      "Average loss at step 112000: 0.549718\n",
      "Average loss at step 114000: 0.553146\n",
      "Average loss at step 116000: 0.555658\n",
      "Average loss at step 118000: 0.551499\n",
      "Average loss at step 120000: 0.554540\n",
      "Nearest to 堤: 窦, 璋, 谣, 儡, 揭, 瘥, 駣, 蛾,\n",
      "Nearest to 谨: 外, 籧, 法, 捧, 代, 嘘, 噪, 中,\n",
      "Nearest to 谛: 翎, 萦, 屠, , 儿, 拄, 胡, 鞯,\n",
      "Nearest to 渌: 鄘, 攥, 疾, 哩, 濠, 妹, 祝, 饩,\n",
      "Nearest to 筒: 疲, 萤, 纵, 焰, 到, 篰, 磅, 蔷,\n",
      "Nearest to 骍: 者, 习, 棣, 篰, 乇, 滪, 共, 货,\n",
      "Nearest to 颣: 厩, 圈, 尖, 莠, 摩, 辽, 扰, 醲,\n",
      "Nearest to 蜘: 簟, 蕾, 幕, 彳, 祲, 蹴, 戊, 咎,\n",
      "Nearest to 呕: 甓, 般, 莫, 囿, 僻, 狮, 店, 秾,\n",
      "Nearest to 泖: 邵, 灸, 诸, 奴, 蓬, 堍, 眇, 鹜,\n",
      "Nearest to 邱: 陷, 嗷, 芰, 匣, 咸, 钳, , 飘,\n",
      "Nearest to 贰: 躞, 裉, 趟, 挥, 亩, 仟, 诫, 不,\n",
      "Nearest to 羊: 帽, 饱, 雏, 眚, 恻, 墓, 擢, 洧,\n",
      "Nearest to 牥: 侈, 纟, 瓃, 旻, 掖, 饭, 浚, 偿,\n",
      "Nearest to 嶓: 鬖, 箬, 皋, 但, 衾, 谳, ④, 郿,\n",
      "Nearest to 愧: 軿, , 敌, 蹒, 傀, 黏, 矧, 凊,\n",
      "Average loss at step 122000: 0.549208\n",
      "Average loss at step 124000: 0.548372\n",
      "Average loss at step 126000: 0.553104\n",
      "Average loss at step 128000: 0.554467\n",
      "Average loss at step 130000: 0.548537\n",
      "Nearest to 堤: 窦, 璋, 谣, 揭, 骆, 儡, 瘥, ⒄,\n",
      "Nearest to 谨: 籧, 外, 法, 捧, 代, 嘘, 噪, 中,\n",
      "Nearest to 谛: 翎, 萦, 屠, , 拄, 儿, 鹥, ,\n",
      "Nearest to 渌: 鄘, 攥, 疾, 哩, 濠, 妹, 祝, 抡,\n",
      "Nearest to 筒: 疲, 纵, 萤, 焰, 到, 磅, 篰, 蔷,\n",
      "Nearest to 骍: 者, 棣, 习, 篰, 乇, 滪, 共, 帨,\n",
      "Nearest to 颣: 厩, 圈, 尖, 摩, 莠, 口, 扰, 醲,\n",
      "Nearest to 蜘: 簟, 蕾, 彳, 幕, 祲, 戊, 咎, 蕣,\n",
      "Nearest to 呕: 甓, 般, 莫, 狮, 囿, 僻, 秾, 淄,\n",
      "Nearest to 泖: 邵, 灸, 诸, 奴, 堍, 蓬, 眇, 鹜,\n",
      "Nearest to 邱: 陷, 嗷, 匣, 钳, 芰, , 立, 咸,\n",
      "Nearest to 贰: 躞, 趟, 裉, 挥, 亩, 不, 诫, 仟,\n",
      "Nearest to 羊: 帽, 饱, 雏, 眚, 恻, 擢, 墓, 洧,\n",
      "Nearest to 牥: 侈, 纟, 瓃, 旻, 掖, 饭, 扬, 浚,\n",
      "Nearest to 嶓: 鬖, 但, 衾, 箬, 皋, 谳, ④, 逶,\n",
      "Nearest to 愧: 軿, , 敌, 蹒, 傀, 黏, 凊, 远,\n",
      "Average loss at step 132000: 0.551822\n",
      "Average loss at step 134000: 0.547676\n",
      "Average loss at step 136000: 0.553894\n",
      "Average loss at step 138000: 0.552029\n",
      "Average loss at step 140000: 0.553842\n",
      "Nearest to 堤: 窦, 璋, 谣, 瘥, 駣, 揭, 儡, 蛾,\n",
      "Nearest to 谨: 籧, 外, 法, 捧, 代, 嘘, 噪, 披,\n",
      "Nearest to 谛: 翎, 萦, 屠, 儿, , 拄, 馌, 胡,\n",
      "Nearest to 渌: 鄘, 攥, 疾, 妹, 濠, 哩, 祝, 抡,\n",
      "Nearest to 筒: 疲, 萤, 纵, 焰, 到, 磅, 篰, 改,\n",
      "Nearest to 骍: 者, 习, 篰, 乇, 滪, 棣, 共, 帨,\n",
      "Nearest to 颣: 厩, 尖, 圈, 摩, 莠, 扰, 醲, 跣,\n",
      "Nearest to 蜘: 簟, 彳, 祲, 幕, 蕾, 蹴, 戊, 刃,\n",
      "Nearest to 呕: 甓, 般, 莫, 狮, 囿, 僻, 淄, 秾,\n",
      "Nearest to 泖: 邵, 灸, 诸, 眇, 蓬, 奴, 堍, 鹜,\n",
      "Nearest to 邱: 陷, 嗷, 匣, 芰, 钳, , 序, 飘,\n",
      "Nearest to 贰: 躞, 趟, 挥, 裉, 亩, 诫, 不, 仟,\n",
      "Nearest to 羊: 帽, 饱, 雏, 眚, 恻, 擢, 墓, 虱,\n",
      "Nearest to 牥: 侈, 纟, 掖, 瓃, 旻, 饭, 鹚, 艨,\n",
      "Nearest to 嶓: 鬖, 但, 箬, 衾, 皋, 谳, ④, 郿,\n",
      "Nearest to 愧: 軿, , 敌, 蹒, 黏, 傀, 凊, 矧,\n",
      "Average loss at step 142000: 0.550743\n",
      "Average loss at step 144000: 0.540687\n",
      "Average loss at step 146000: 0.552888\n",
      "Average loss at step 148000: 0.546912\n",
      "Average loss at step 150000: 0.553584\n",
      "Nearest to 堤: 窦, 璋, 谣, 揭, 儡, 瘥, 蛾, 骆,\n",
      "Nearest to 谨: 外, 籧, 法, 代, 捧, 嘘, 噪, 发,\n",
      "Nearest to 谛: 翎, 萦, 屠, , 儿, 拄, 馌, 胡,\n",
      "Nearest to 渌: 鄘, 哩, 疾, 攥, 濠, 祝, 妹, 脂,\n",
      "Nearest to 筒: 疲, 萤, 纵, 焰, 到, 磅, 篰, 赪,\n",
      "Nearest to 骍: 者, 乇, 习, 滪, 棣, 篰, 共, 骊,\n",
      "Nearest to 颣: 厩, 尖, 摩, 圈, 莠, 辽, 醲, 口,\n",
      "Nearest to 蜘: 簟, 蕾, 幕, 祲, 彳, 蹴, 戊, 蕣,\n",
      "Nearest to 呕: 甓, 般, 莫, 狮, 僻, 囿, 秾, 淄,\n",
      "Nearest to 泖: 邵, 灸, 诸, 奴, 蓬, 眇, 窜, 堍,\n",
      "Nearest to 邱: 陷, 嗷, 匣, , 飘, 芰, 钳, 序,\n",
      "Nearest to 贰: 躞, 趟, 挥, 裉, 不, 亩, 诫, 仟,\n",
      "Nearest to 羊: 帽, 饱, 雏, 眚, 恻, 墓, 擢, 窗,\n",
      "Nearest to 牥: 侈, 纟, 瓃, 旻, 掖, 浚, 饭, 艨,\n",
      "Nearest to 嶓: 鬖, 但, 箬, 皋, 衾, ④, 谳, 郿,\n",
      "Nearest to 愧: 軿, , 敌, 蹒, 傀, 黏, 远, 矧,\n",
      "Average loss at step 152000: 0.554952\n",
      "Average loss at step 154000: 0.544568\n",
      "Average loss at step 156000: 0.555410\n",
      "Average loss at step 158000: 0.543976\n",
      "Average loss at step 160000: 0.548973\n",
      "Nearest to 堤: 窦, 璋, 谣, 瘥, 揭, 儡, 駣, 蛾,\n",
      "Nearest to 谨: 外, 籧, 法, 捧, 代, 嘘, 噪, 发,\n",
      "Nearest to 谛: 翎, 萦, 屠, 儿, , 拄, 瘥, 胡,\n",
      "Nearest to 渌: 鄘, 攥, 疾, 濠, 哩, 妹, 祝, 抡,\n",
      "Nearest to 筒: 疲, 萤, 纵, 焰, 到, 磅, 篰, 赪,\n",
      "Nearest to 骍: 者, 习, 滪, 乇, 篰, 棣, 聚, 共,\n",
      "Nearest to 颣: 厩, 尖, 摩, 圈, 莠, 醲, 辕, 扰,\n",
      "Nearest to 蜘: 簟, 蕾, 幕, 祲, 彳, 咎, 戊, 蹴,\n",
      "Nearest to 呕: 甓, 般, 莫, 狮, 僻, 囿, 秾, 淄,\n",
      "Nearest to 泖: 邵, 灸, 诸, 奴, 蓬, 眇, 堍, 眄,\n",
      "Nearest to 邱: 陷, 嗷, 芰, 匣, 钳, 飘, 咸, ,\n",
      "Nearest to 贰: 躞, 趟, 挥, 裉, 诫, 亩, 不, 仟,\n",
      "Nearest to 羊: 帽, 饱, 雏, 眚, 恻, 墓, 擢, 虱,\n",
      "Nearest to 牥: 侈, 纟, 瓃, 旻, 偿, 掖, 饭, 浚,\n",
      "Nearest to 嶓: 鬖, 箬, 但, 皋, 衾, ④, 谳, 徨,\n",
      "Nearest to 愧: 軿, , 敌, 蹒, 傀, 黏, 远, 矧,\n",
      "Average loss at step 162000: 0.547834\n",
      "Average loss at step 164000: 0.549343\n",
      "Average loss at step 166000: 0.554746\n",
      "Average loss at step 168000: 0.545908\n",
      "Average loss at step 170000: 0.550758\n",
      "Nearest to 堤: 窦, 璋, 谣, 儡, 瘥, 揭, 蛾, 骆,\n",
      "Nearest to 谨: 外, 籧, 法, 捧, 代, 嘘, 噪, 中,\n",
      "Nearest to 谛: 翎, 萦, 屠, , 儿, 拄, 馌, 胡,\n",
      "Nearest to 渌: 鄘, 攥, 疾, 濠, 妹, 哩, 抡, 祝,\n",
      "Nearest to 筒: 疲, 萤, 纵, 到, 焰, 磅, 篰, 蔷,\n",
      "Nearest to 骍: 者, 滪, 习, 棣, 乇, 篰, 共, 骊,\n",
      "Nearest to 颣: 厩, 尖, 圈, 摩, 莠, 扰, 醲, 口,\n",
      "Nearest to 蜘: 簟, 蕾, 彳, 幕, 祲, 蹴, 戊, 迸,\n",
      "Nearest to 呕: 般, 甓, 莫, 狮, 囿, 僻, 淄, 秾,\n",
      "Nearest to 泖: 邵, 灸, 诸, 蓬, 眇, 奴, 堍, 鹜,\n",
      "Nearest to 邱: 陷, 嗷, 匣, 芰, 立, 钳, 飘, 序,\n",
      "Nearest to 贰: 躞, 趟, 裉, 挥, 亩, 诫, 不, 鵕,\n",
      "Nearest to 羊: 帽, 饱, 雏, 眚, 恻, 墓, 擢, 洧,\n",
      "Nearest to 牥: 侈, 纟, 瓃, 旻, 掖, 饭, 鹚, 浚,\n",
      "Nearest to 嶓: 鬖, 箬, 但, 皋, 衾, 谳, ④, 徨,\n",
      "Nearest to 愧: 軿, , 敌, 蹒, 傀, 黏, 靸, 矧,\n",
      "Average loss at step 172000: 0.548236\n",
      "Average loss at step 174000: 0.548697\n",
      "Average loss at step 176000: 0.547511\n",
      "Average loss at step 178000: 0.542137\n",
      "Average loss at step 180000: 0.551994\n",
      "Nearest to 堤: 窦, 璋, 谣, 瘥, 儡, 揭, 駣, 尻,\n",
      "Nearest to 谨: 外, 籧, 法, 捧, 代, 嘘, 噪, 中,\n",
      "Nearest to 谛: 翎, 萦, 屠, 儿, , 拄, 鞯, 胡,\n",
      "Nearest to 渌: 鄘, 攥, 疾, 濠, 哩, 妹, 祝, 沦,\n",
      "Nearest to 筒: 疲, 萤, 纵, 焰, 到, 篰, 磅, 蔷,\n",
      "Nearest to 骍: 者, 习, 篰, 乇, 棣, 滪, 共, 帨,\n",
      "Nearest to 颣: 厩, 尖, 圈, 摩, 莠, 滋, 醲, 按,\n",
      "Nearest to 蜘: 簟, 蕾, 幕, 祲, 彳, 蹴, 迸, 戊,\n",
      "Nearest to 呕: 甓, 般, 莫, 囿, 狮, 僻, 秾, 店,\n",
      "Nearest to 泖: 邵, 灸, 诸, 蓬, 眇, 奴, 眄, 堍,\n",
      "Nearest to 邱: 陷, 嗷, 匣, 芰, 立, 钳, , 飘,\n",
      "Nearest to 贰: 躞, 趟, 挥, 诫, 不, 亩, 裉, 仟,\n",
      "Nearest to 羊: 雏, 帽, 饱, 眚, 恻, 墓, 擢, 虱,\n",
      "Nearest to 牥: 侈, 纟, 瓃, 旻, 7, 饭, 掖, 鹚,\n",
      "Nearest to 嶓: 鬖, 箬, 但, 皋, 衾, ④, 谳, 郿,\n",
      "Nearest to 愧: 軿, , 敌, 蹒, 傀, 黏, 矧, 凊,\n",
      "Average loss at step 182000: 0.544023\n",
      "Average loss at step 184000: 0.551087\n",
      "Average loss at step 186000: 0.544316\n",
      "Average loss at step 188000: 0.542448\n",
      "Average loss at step 190000: 0.546890\n",
      "Nearest to 堤: 窦, 璋, 儡, 谣, 揭, 瘥, 駣, 蛾,\n",
      "Nearest to 谨: 外, 籧, 法, 捧, 代, 嘘, 噪, 淦,\n",
      "Nearest to 谛: 翎, 萦, 屠, 儿, , 拄, 桹, 胡,\n",
      "Nearest to 渌: 鄘, 攥, 疾, 濠, 哩, 脂, 妹, 祝,\n",
      "Nearest to 筒: 疲, 萤, 纵, 到, 焰, 篰, 磅, 蔷,\n",
      "Nearest to 骍: 者, 习, 乇, 篰, 棣, 滪, 共, 帨,\n",
      "Nearest to 颣: 厩, 尖, 圈, 摩, 莠, 滋, 口, 醲,\n",
      "Nearest to 蜘: 簟, 蕾, 祲, 幕, 彳, 蹴, 戊, 迸,\n",
      "Nearest to 呕: 般, 甓, 莫, 狮, 囿, 僻, 秾, 淄,\n",
      "Nearest to 泖: 邵, 灸, 诸, 奴, 眇, 蓬, 堍, 窜,\n",
      "Nearest to 邱: 陷, 嗷, 匣, 芰, 飘, 序, 钳, ,\n",
      "Nearest to 贰: 躞, 趟, 裉, 挥, 亩, 不, 诫, 仟,\n",
      "Nearest to 羊: 帽, 饱, 雏, 眚, 恻, 墓, 擢, 窗,\n",
      "Nearest to 牥: 侈, 纟, 瓃, 鹚, 旻, 饭, 掖, 7,\n",
      "Nearest to 嶓: 鬖, 箬, 皋, 衾, 但, ④, 谳, 逶,\n",
      "Nearest to 愧: 軿, , 敌, 蹒, 傀, 黏, 矧, 溅,\n",
      "Average loss at step 192000: 0.549124\n",
      "Average loss at step 194000: 0.551077\n",
      "Average loss at step 196000: 0.549712\n",
      "Average loss at step 198000: 0.541765\n",
      "Average loss at step 200000: 0.555306\n",
      "Nearest to 堤: 窦, 璋, 谣, 儡, 揭, 瘥, 駣, 蛾,\n",
      "Nearest to 谨: 外, 籧, 法, 捧, 代, 嘘, 噪, 发,\n",
      "Nearest to 谛: 翎, 萦, 屠, 儿, , 拄, 厌, 鹥,\n",
      "Nearest to 渌: 鄘, 攥, 疾, 濠, 妹, 哩, 祝, 脂,\n",
      "Nearest to 筒: 疲, 萤, 纵, 焰, 到, 蔷, 篰, 磅,\n",
      "Nearest to 骍: 者, 习, 乇, 棣, 篰, 滪, 共, 帨,\n",
      "Nearest to 颣: 厩, 尖, 圈, 摩, 莠, 口, 扰, 醲,\n",
      "Nearest to 蜘: 簟, 蕾, 幕, 祲, 彳, 蹴, 戊, 閤,\n",
      "Nearest to 呕: 般, 甓, 莫, 囿, 狮, 僻, 淄, 秾,\n",
      "Nearest to 泖: 邵, 灸, 诸, 奴, 蓬, 眇, 堍, 窜,\n",
      "Nearest to 邱: 陷, 嗷, 芰, 匣, 立, 钳, 飘, 序,\n",
      "Nearest to 贰: 躞, 趟, 挥, 诫, 裉, 亩, 不, 仟,\n",
      "Nearest to 羊: 帽, 雏, 饱, 眚, 恻, 墓, 擢, 虱,\n",
      "Nearest to 牥: 侈, 纟, 瓃, 旻, 饭, 掖, 鹚, 7,\n",
      "Nearest to 嶓: 鬖, 箬, 皋, 但, 衾, 谳, ④, 逶,\n",
      "Nearest to 愧: 軿, , 敌, 蹒, 傀, 黏, 矧, 凊,\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average loss at step 202000: 0.543367\n",
      "Average loss at step 204000: 0.548471\n",
      "Average loss at step 206000: 0.553128\n",
      "Average loss at step 208000: 0.546020\n",
      "Average loss at step 210000: 0.552263\n",
      "Nearest to 堤: 窦, 璋, 瘥, 谣, 儡, 揭, 骆, 駣,\n",
      "Nearest to 谨: 外, 籧, 法, 代, 捧, 嘘, 噪, 榛,\n",
      "Nearest to 谛: 翎, 萦, 屠, 儿, , 拄, 悒, 瘥,\n",
      "Nearest to 渌: 鄘, 攥, 濠, 疾, 祝, 哩, 妹, 脂,\n",
      "Nearest to 筒: 疲, 纵, 萤, 磅, 焰, 到, 篰, 蔷,\n",
      "Nearest to 骍: 者, 习, 棣, 乇, 篰, 滪, 帨, 共,\n",
      "Nearest to 颣: 厩, 摩, 尖, 圈, 莠, 滋, 醲, 口,\n",
      "Nearest to 蜘: 簟, 蕾, 幕, 彳, 祲, 戊, 迸, 蹴,\n",
      "Nearest to 呕: 甓, 般, 莫, 囿, 狮, 僻, 淄, 攘,\n",
      "Nearest to 泖: 邵, 灸, 诸, 眇, 蓬, 堍, 奴, 眄,\n",
      "Nearest to 邱: 陷, 嗷, 匣, 芰, 钳, , 飘, 立,\n",
      "Nearest to 贰: 躞, 趟, 挥, 裉, 不, 诫, 亩, 仟,\n",
      "Nearest to 羊: 帽, 饱, 眚, 雏, 恻, 墓, 擢, 虱,\n",
      "Nearest to 牥: 侈, 纟, 瓃, 掖, 鹚, 旻, 饭, 偿,\n",
      "Nearest to 嶓: 鬖, 但, 箬, 皋, 衾, 谳, ④, 郿,\n",
      "Nearest to 愧: 軿, , 敌, 蹒, 黏, 矧, 傀, 凊,\n",
      "Average loss at step 212000: 0.541434\n",
      "Average loss at step 214000: 0.543270\n",
      "Average loss at step 216000: 0.554092\n",
      "Average loss at step 218000: 0.548248\n",
      "Average loss at step 220000: 0.553963\n",
      "Nearest to 堤: 窦, 璋, 谣, 揭, 儡, 瘥, 尻, 駣,\n",
      "Nearest to 谨: 外, 籧, 法, 捧, 代, 嘘, 噪, 中,\n",
      "Nearest to 谛: 翎, 萦, 屠, , 拄, 儿, 悒, 胡,\n",
      "Nearest to 渌: 鄘, 攥, 疾, 濠, 哩, 妹, 祝, 脂,\n",
      "Nearest to 筒: 疲, 纵, 萤, 到, 焰, 篰, 磅, 蔷,\n",
      "Nearest to 骍: 者, 习, 乇, 棣, 篰, 滪, 共, 帨,\n",
      "Nearest to 颣: 厩, 尖, 摩, 圈, 莠, 口, 滋, 醲,\n",
      "Nearest to 蜘: 簟, 蕾, 祲, 幕, 彳, 咎, 戊, 迸,\n",
      "Nearest to 呕: 甓, 般, 莫, 僻, 攘, 狮, 囿, 店,\n",
      "Nearest to 泖: 邵, 灸, 诸, 奴, 眇, 蓬, 狞, 堍,\n",
      "Nearest to 邱: 陷, 嗷, 匣, 芰, 飘, 立, 序, ,\n",
      "Nearest to 贰: 躞, 趟, 挥, 裉, 诫, 不, 仟, 亩,\n",
      "Nearest to 羊: 帽, 饱, 雏, 眚, 恻, 擢, 墓, 虱,\n",
      "Nearest to 牥: 侈, 纟, 瓃, 旻, 掖, 饭, 鹚, 艨,\n",
      "Nearest to 嶓: 鬖, 皋, 衾, 但, 箬, 谳, ④, 徨,\n",
      "Nearest to 愧: 軿, , 敌, 傀, 蹒, 黏, 矧, 凊,\n",
      "Average loss at step 222000: 0.541399\n",
      "Average loss at step 224000: 0.548713\n",
      "Average loss at step 226000: 0.549004\n",
      "Average loss at step 228000: 0.537662\n",
      "Average loss at step 230000: 0.549649\n",
      "Nearest to 堤: 窦, 璋, 谣, 儡, 揭, 瘥, 駣, 艺,\n",
      "Nearest to 谨: 外, 籧, 法, 代, 捧, 嘘, 噪, 榛,\n",
      "Nearest to 谛: 翎, 萦, 屠, 拄, 儿, , 胡, 翡,\n",
      "Nearest to 渌: 鄘, 攥, 濠, 疾, 妹, 哩, 祝, 逼,\n",
      "Nearest to 筒: 疲, 萤, 纵, 焰, 到, 磅, 篰, 哺,\n",
      "Nearest to 骍: 者, 习, 滪, 乇, 棣, 篰, 共, 茕,\n",
      "Nearest to 颣: 厩, 摩, 尖, 圈, 莠, 口, 滋, 醲,\n",
      "Nearest to 蜘: 簟, 蕾, 彳, 幕, 祲, 戊, 蹴, 咎,\n",
      "Nearest to 呕: 般, 甓, 莫, 僻, 囿, 狮, 秾, 攘,\n",
      "Nearest to 泖: 邵, 灸, 诸, 眇, 蓬, 奴, 眄, 堍,\n",
      "Nearest to 邱: 陷, 嗷, 匣, 芰, 飘, 立, 序, ,\n",
      "Nearest to 贰: 躞, 趟, 挥, 亩, 裉, 不, 诫, 鵕,\n",
      "Nearest to 羊: 帽, 饱, 眚, 恻, 雏, 墓, 擢, 洧,\n",
      "Nearest to 牥: 侈, 纟, 瓃, 旻, 7, 掖, 鹚, 饭,\n",
      "Nearest to 嶓: 鬖, 箬, 但, 皋, 衾, ④, 谳, 郿,\n",
      "Nearest to 愧: 軿, , 敌, 蹒, 矧, 黏, 傀, 凊,\n",
      "Average loss at step 232000: 0.538904\n",
      "Average loss at step 234000: 0.552294\n",
      "Average loss at step 236000: 0.552533\n",
      "Average loss at step 238000: 0.545539\n",
      "Average loss at step 240000: 0.545686\n",
      "Nearest to 堤: 窦, 璋, 揭, 儡, 谣, 瘥, 艺, 駣,\n",
      "Nearest to 谨: 外, 籧, 法, 捧, 代, 嘘, 噪, 发,\n",
      "Nearest to 谛: 翎, 萦, 屠, 儿, 拄, , 翡, 胡,\n",
      "Nearest to 渌: 鄘, 疾, 攥, 濠, 哩, 妹, 祝, 抡,\n",
      "Nearest to 筒: 疲, 萤, 纵, 篰, 到, 焰, 磅, 蔷,\n",
      "Nearest to 骍: 者, 篰, 乇, 习, 棣, 滪, 帨, 共,\n",
      "Nearest to 颣: 厩, 尖, 摩, 圈, 莠, 滋, 口, 醲,\n",
      "Nearest to 蜘: 簟, 蕾, 幕, 祲, 戊, 彳, 蹴, 迸,\n",
      "Nearest to 呕: 甓, 般, 莫, 狮, 僻, 囿, 店, 秾,\n",
      "Nearest to 泖: 邵, 灸, 诸, 蓬, 奴, 眇, 眄, 堍,\n",
      "Nearest to 邱: 陷, 嗷, 匣, 芰, 序, , 立, 飘,\n",
      "Nearest to 贰: 躞, 趟, 挥, 诫, 亩, 不, 裉, 鵕,\n",
      "Nearest to 羊: 帽, 雏, 饱, 眚, 恻, 擢, 墓, 虱,\n",
      "Nearest to 牥: 侈, 纟, 瓃, 饭, 旻, 掖, 鹚, 偿,\n",
      "Nearest to 嶓: 鬖, 箬, 皋, 但, 衾, 谳, ④, 逶,\n",
      "Nearest to 愧: 軿, , 敌, 蹒, 黏, 傀, 矧, 凊,\n",
      "Average loss at step 242000: 0.540849\n",
      "Average loss at step 244000: 0.549285\n",
      "Average loss at step 246000: 0.539255\n",
      "Average loss at step 248000: 0.543477\n",
      "Average loss at step 250000: 0.552586\n",
      "Nearest to 堤: 窦, 璋, 谣, 瘥, 儡, 揭, 蛾, 駣,\n",
      "Nearest to 谨: 外, 籧, 法, 捧, 代, 嘘, 噪, 榛,\n",
      "Nearest to 谛: 翎, 萦, 屠, 儿, , 拄, 悒, 胡,\n",
      "Nearest to 渌: 鄘, 攥, 疾, 濠, 哩, 祝, 妹, 沦,\n",
      "Nearest to 筒: 疲, 纵, 萤, 焰, 到, 哺, 篰, 磅,\n",
      "Nearest to 骍: 者, 棣, 习, 乇, 篰, 滪, 共, 骊,\n",
      "Nearest to 颣: 厩, 摩, 尖, 圈, 莠, 醲, 辽, 口,\n",
      "Nearest to 蜘: 簟, 蕾, 幕, 祲, 蹴, 彳, 戊, 迸,\n",
      "Nearest to 呕: 甓, 般, 莫, 僻, 囿, 狮, 攘, 秾,\n",
      "Nearest to 泖: 邵, 灸, 诸, 蓬, 眇, 奴, 狞, 堍,\n",
      "Nearest to 邱: 陷, 嗷, 匣, 芰, 飘, 序, , 立,\n",
      "Nearest to 贰: 躞, 趟, 挥, 诫, 亩, 裉, 不, 鵕,\n",
      "Nearest to 羊: 帽, 饱, 雏, 眚, 恻, 擢, 墓, 洧,\n",
      "Nearest to 牥: 侈, 纟, 鹚, 旻, 瓃, 7, 饭, 掖,\n",
      "Nearest to 嶓: 鬖, 皋, 箬, 但, 衾, 谳, ④, 郿,\n",
      "Nearest to 愧: 軿, , 敌, 蹒, 黏, 矧, 傀, 凊,\n",
      "Average loss at step 252000: 0.541504\n",
      "Average loss at step 254000: 0.552374\n",
      "Average loss at step 256000: 0.538657\n",
      "Average loss at step 258000: 0.549400\n",
      "Average loss at step 260000: 0.547696\n",
      "Nearest to 堤: 窦, 璋, 瘥, 谣, 揭, 儡, ⒄, 艺,\n",
      "Nearest to 谨: 外, 籧, 法, 代, 捧, 噪, 嘘, 榛,\n",
      "Nearest to 谛: 萦, 翎, 屠, 儿, , 拄, 瘥, 胡,\n",
      "Nearest to 渌: 鄘, 攥, 疾, 濠, 妹, 哩, 祝, 逼,\n",
      "Nearest to 筒: 疲, 萤, 纵, 到, 焰, 篰, 磅, 樻,\n",
      "Nearest to 骍: 者, 乇, 习, 棣, 篰, 滪, 帨, 共,\n",
      "Nearest to 颣: 厩, 摩, 尖, 圈, 莠, 醲, 口, 扰,\n",
      "Nearest to 蜘: 簟, 蕾, 幕, 祲, 彳, 戊, 迸, 蹴,\n",
      "Nearest to 呕: 般, 甓, 莫, 囿, 僻, 店, 淄, 狮,\n",
      "Nearest to 泖: 邵, 诸, 灸, 奴, 蓬, 眇, 堍, 眄,\n",
      "Nearest to 邱: 陷, 嗷, 匣, 立, 芰, 序, 飘, 仟,\n",
      "Nearest to 贰: 躞, 趟, 挥, 诫, 仟, 不, 亩, 裉,\n",
      "Nearest to 羊: 帽, 雏, 饱, 眚, 恻, 擢, 墓, 洧,\n",
      "Nearest to 牥: 侈, 纟, 鹚, 瓃, 旻, 饭, 掖, 浚,\n",
      "Nearest to 嶓: 鬖, 箬, 皋, 但, 衾, 谳, ④, 逶,\n",
      "Nearest to 愧: 軿, , 敌, 蹒, 黏, 矧, 傀, 凊,\n",
      "Average loss at step 262000: 0.544723\n",
      "Average loss at step 264000: 0.542870\n",
      "Average loss at step 266000: 0.535889\n",
      "Average loss at step 268000: 0.549382\n",
      "Average loss at step 270000: 0.547972\n",
      "Nearest to 堤: 窦, 璋, 谣, 瘥, 儡, 揭, 艺, 駣,\n",
      "Nearest to 谨: 外, 籧, 代, 法, 捧, 噪, 嘘, 中,\n",
      "Nearest to 谛: 翎, 萦, 儿, 屠, , 拄, 胡, 翡,\n",
      "Nearest to 渌: 鄘, 攥, 濠, 疾, 哩, 妹, 祝, 沦,\n",
      "Nearest to 筒: 疲, 萤, 纵, 到, 磅, 焰, 篰, 哺,\n",
      "Nearest to 骍: 者, 习, 棣, 篰, 乇, 滪, 共, 聚,\n",
      "Nearest to 颣: 厩, 摩, 尖, 莠, 圈, 滋, 扰, 醲,\n",
      "Nearest to 蜘: 簟, 蕾, 彳, 幕, 祲, 戊, 蹴, 咎,\n",
      "Nearest to 呕: 般, 甓, 莫, 僻, 囿, 淄, 狮, 秾,\n",
      "Nearest to 泖: 邵, 灸, 诸, 奴, 蓬, 眇, 堍, 眄,\n",
      "Nearest to 邱: 陷, 嗷, 匣, 芰, 煜, 立, 序, 飘,\n",
      "Nearest to 贰: 躞, 趟, 挥, 诫, 亩, 不, 裉, 仟,\n",
      "Nearest to 羊: 帽, 饱, 眚, 雏, 恻, 擢, 墓, 洧,\n",
      "Nearest to 牥: 侈, 纟, 瓃, 鹚, 旻, 掖, 饭, 艨,\n",
      "Nearest to 嶓: 鬖, 皋, 箬, 但, 衾, 谳, ④, 郿,\n",
      "Nearest to 愧: 軿, , 蹒, 敌, 黏, 矧, 傀, 凊,\n",
      "Average loss at step 272000: 0.541558\n",
      "Average loss at step 274000: 0.544936\n",
      "Average loss at step 276000: 0.540150\n",
      "Average loss at step 278000: 0.543849\n",
      "Average loss at step 280000: 0.544542\n",
      "Nearest to 堤: 窦, 璋, 儡, 揭, 谣, 瘥, 蛾, 駣,\n",
      "Nearest to 谨: 外, 籧, 法, 代, 捧, 嘘, 噪, 榛,\n",
      "Nearest to 谛: 翎, 屠, 萦, 儿, 拄, , 胡, 献,\n",
      "Nearest to 渌: 鄘, 疾, 攥, 哩, 濠, 妹, 祝, 沦,\n",
      "Nearest to 筒: 疲, 萤, 纵, 到, 篰, 焰, 磅, 哺,\n",
      "Nearest to 骍: 者, 习, 滪, 棣, 乇, 篰, 共, 茕,\n",
      "Nearest to 颣: 厩, 摩, 圈, 尖, 莠, 醲, 滋, 扰,\n",
      "Nearest to 蜘: 簟, 蕾, 蹴, 幕, 祲, 彳, 戊, 咎,\n",
      "Nearest to 呕: 般, 莫, 甓, 僻, 囿, 淄, 狮, 秾,\n",
      "Nearest to 泖: 邵, 灸, 诸, 奴, 蓬, 眇, 堍, 眄,\n",
      "Nearest to 邱: 陷, 嗷, 芰, 匣, 立, 序, 飘, 仟,\n",
      "Nearest to 贰: 躞, 趟, 挥, 亩, 诫, 不, 仟, 裉,\n",
      "Nearest to 羊: 帽, 饱, 眚, 恻, 雏, 墓, 擢, 洧,\n",
      "Nearest to 牥: 侈, 纟, 瓃, 旻, 鹚, 掖, 浚, 饭,\n",
      "Nearest to 嶓: 鬖, 衾, 皋, 但, 箬, ④, 谳, 逶,\n",
      "Nearest to 愧: 軿, , 敌, 蹒, 黏, 矧, 傀, 凊,\n",
      "Average loss at step 282000: 0.549759\n",
      "Average loss at step 284000: 0.551607\n",
      "Average loss at step 286000: 0.543475\n",
      "Average loss at step 288000: 0.545937\n",
      "Average loss at step 290000: 0.545947\n",
      "Nearest to 堤: 窦, 璋, 瘥, 揭, 谣, 儡, 尻, 駣,\n",
      "Nearest to 谨: 外, 籧, 法, 代, 捧, 嘘, 噪, 发,\n",
      "Nearest to 谛: 翎, 屠, 萦, 拄, 儿, , 胡, 馌,\n",
      "Nearest to 渌: 鄘, 攥, 濠, 疾, 妹, 祝, 哩, 谠,\n",
      "Nearest to 筒: 疲, 萤, 纵, 到, 焰, 哺, 篰, 磅,\n",
      "Nearest to 骍: 者, 习, 滪, 乇, 棣, 篰, 共, 帨,\n",
      "Nearest to 颣: 厩, 尖, 圈, 摩, 莠, 滋, 醲, 口,\n",
      "Nearest to 蜘: 簟, 蕾, 幕, 彳, 祲, 戊, 蹴, 迸,\n",
      "Nearest to 呕: 般, 甓, 莫, 僻, 狮, 囿, 淄, 攘,\n",
      "Nearest to 泖: 邵, 灸, 诸, 蓬, 奴, 眇, 堍, 眄,\n",
      "Nearest to 邱: 陷, 嗷, 芰, 立, 匣, 飘, 序, ,\n",
      "Nearest to 贰: 躞, 趟, 挥, 诫, 不, 亩, 仟, 裉,\n",
      "Nearest to 羊: 帽, 雏, 饱, 眚, 恻, 擢, 墓, 洧,\n",
      "Nearest to 牥: 侈, 纟, 瓃, 鹚, 旻, 掖, 偿, 饭,\n",
      "Nearest to 嶓: 鬖, 皋, 箬, 但, 衾, ④, 谳, 逶,\n",
      "Nearest to 愧: 軿, 蹒, , 敌, 黏, 傀, 矧, 凊,\n",
      "Average loss at step 292000: 0.543388\n",
      "Average loss at step 294000: 0.545475\n",
      "Average loss at step 296000: 0.544306\n",
      "Average loss at step 298000: 0.543187\n",
      "Average loss at step 300000: 0.541745\n",
      "Nearest to 堤: 窦, 璋, 谣, 瘥, 揭, 儡, ⒄, 蛾,\n",
      "Nearest to 谨: 外, 法, 籧, 代, 捧, 噪, 嘘, 潸,\n",
      "Nearest to 谛: 翎, 萦, 屠, 儿, 拄, , 胡, 鹥,\n",
      "Nearest to 渌: 鄘, 濠, 攥, 疾, 祝, 妹, 哩, 沦,\n",
      "Nearest to 筒: 疲, 萤, 纵, 到, 篰, 焰, 磅, 赪,\n",
      "Nearest to 骍: 者, 习, 篰, 棣, 乇, 滪, 共, 帨,\n",
      "Nearest to 颣: 厩, 摩, 尖, 圈, 莠, 醲, 扰, 按,\n",
      "Nearest to 蜘: 簟, 蕾, 幕, 祲, 彳, 蹴, 戊, 迸,\n",
      "Nearest to 呕: 甓, 般, 莫, 僻, 囿, 攘, 秾, 狮,\n",
      "Nearest to 泖: 邵, 灸, 诸, 奴, 蓬, 眇, 堍, 眄,\n",
      "Nearest to 邱: 陷, 嗷, 匣, 芰, 立, 飘, , 序,\n",
      "Nearest to 贰: 躞, 趟, 诫, 挥, 不, 亩, 裉, 仟,\n",
      "Nearest to 羊: 帽, 雏, 饱, 眚, 恻, 墓, 擢, 窗,\n",
      "Nearest to 牥: 侈, 纟, 瓃, 旻, 鹚, 饭, 掖, 浚,\n",
      "Nearest to 嶓: 鬖, 箬, 皋, 但, 衾, 谳, ④, 逶,\n",
      "Nearest to 愧: 軿, , 敌, 蹒, 黏, 矧, 傀, 凊,\n",
      "Average loss at step 302000: 0.546476\n",
      "Average loss at step 304000: 0.546474\n",
      "Average loss at step 306000: 0.543120\n",
      "Average loss at step 308000: 0.547900\n",
      "Average loss at step 310000: 0.542664\n",
      "Nearest to 堤: 窦, 璋, 谣, 瘥, 揭, 儡, 艺, ⒄,\n",
      "Nearest to 谨: 外, 法, 籧, 代, 捧, 嘘, 噪, 粱,\n",
      "Nearest to 谛: 翎, 萦, 屠, 儿, 拄, , 翡, 悒,\n",
      "Nearest to 渌: 鄘, 攥, 疾, 濠, 妹, 哩, 祝, 沦,\n",
      "Nearest to 筒: 疲, 萤, 纵, 到, 篰, 磅, 焰, 哺,\n",
      "Nearest to 骍: 者, 习, 棣, 篰, 乇, 滪, 骊, 帨,\n",
      "Nearest to 颣: 厩, 尖, 摩, 圈, 莠, 醲, 扰, 滋,\n",
      "Nearest to 蜘: 簟, 蕾, 幕, 祲, 蹴, 彳, 戊, 迸,\n",
      "Nearest to 呕: 般, 甓, 莫, 囿, 僻, 秾, 淄, 狮,\n",
      "Nearest to 泖: 邵, 灸, 诸, 奴, 眇, 蓬, 堍, 窜,\n",
      "Nearest to 邱: 陷, 嗷, 匣, 芰, 立, 序, 飘, ,\n",
      "Nearest to 贰: 躞, 趟, 挥, 诫, 不, 亩, 仟, 裉,\n",
      "Nearest to 羊: 帽, 眚, 恻, 饱, 雏, 擢, 墓, 虱,\n",
      "Nearest to 牥: 侈, 纟, 瓃, 饭, 鹚, 旻, 掖, 艨,\n",
      "Nearest to 嶓: 鬖, 箬, 皋, 但, 衾, ④, 谳, 逶,\n",
      "Nearest to 愧: 軿, , 敌, 蹒, 黏, 矧, 傀, 凊,\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average loss at step 312000: 0.548161\n",
      "Average loss at step 314000: 0.545613\n",
      "Average loss at step 316000: 0.536072\n",
      "Average loss at step 318000: 0.544752\n",
      "Average loss at step 320000: 0.538138\n",
      "Nearest to 堤: 窦, 璋, 谣, 瘥, 揭, 儡, 艺, ⒄,\n",
      "Nearest to 谨: 外, 籧, 代, 法, 捧, 噪, 嘘, 悠,\n",
      "Nearest to 谛: 翎, 萦, 屠, 拄, 儿, , 悒, 缩,\n",
      "Nearest to 渌: 鄘, 攥, 疾, 濠, 妹, 哩, 沦, 祝,\n",
      "Nearest to 筒: 疲, 萤, 纵, 到, 磅, 篰, 焰, 樻,\n",
      "Nearest to 骍: 者, 习, 棣, 篰, 乇, 滪, 共, 骊,\n",
      "Nearest to 颣: 厩, 摩, 尖, 圈, 莠, 醲, 颅, 扰,\n",
      "Nearest to 蜘: 簟, 蕾, 蹴, 幕, 祲, 戊, 彳, 迸,\n",
      "Nearest to 呕: 莫, 般, 甓, 囿, 僻, 狮, 秾, 淄,\n",
      "Nearest to 泖: 邵, 灸, 诸, 蓬, 眇, 奴, 堍, 眄,\n",
      "Nearest to 邱: 陷, 嗷, 匣, 芰, , 立, 飘, 序,\n",
      "Nearest to 贰: 趟, 躞, 挥, 诫, 不, 亩, 仟, 裉,\n",
      "Nearest to 羊: 帽, 眚, 饱, 雏, 恻, 墓, 擢, 虱,\n",
      "Nearest to 牥: 侈, 纟, 鹚, 瓃, 偿, 旻, 饭, 浚,\n",
      "Nearest to 嶓: 鬖, 箬, 衾, 但, 皋, 谳, ④, 逶,\n",
      "Nearest to 愧: 軿, , 蹒, 敌, 凊, 黏, 矧, 傀,\n",
      "Average loss at step 322000: 0.539118\n",
      "Average loss at step 324000: 0.547696\n",
      "Average loss at step 326000: 0.544849\n",
      "Average loss at step 328000: 0.547184\n",
      "Average loss at step 330000: 0.538929\n",
      "Nearest to 堤: 窦, 璋, 谣, 儡, 揭, 瘥, ⒄, 艺,\n",
      "Nearest to 谨: 外, 籧, 代, 法, 捧, 噪, 嘘, 榛,\n",
      "Nearest to 谛: 翎, 萦, 屠, 儿, 拄, , 翡, 胡,\n",
      "Nearest to 渌: 鄘, 攥, 疾, 濠, 妹, 哩, 沦, 祝,\n",
      "Nearest to 筒: 疲, 萤, 纵, 篰, 到, 磅, 哺, 焰,\n",
      "Nearest to 骍: 者, 篰, 习, 乇, 滪, 棣, 帨, 共,\n",
      "Nearest to 颣: 厩, 摩, 尖, 莠, 圈, 醲, 口, 滋,\n",
      "Nearest to 蜘: 簟, 蕾, 幕, 彳, 祲, 戊, 迸, 蹴,\n",
      "Nearest to 呕: 莫, 般, 甓, 僻, 囿, 淄, 狮, 店,\n",
      "Nearest to 泖: 邵, 灸, 诸, 奴, 眇, 蓬, 狞, 堍,\n",
      "Nearest to 邱: 陷, 嗷, 芰, 匣, 飘, , 立, 序,\n",
      "Nearest to 贰: 躞, 趟, 诫, 挥, 不, 裉, 亩, 仟,\n",
      "Nearest to 羊: 帽, 眚, 饱, 雏, 恻, 墓, 擢, 窗,\n",
      "Nearest to 牥: 侈, 纟, 瓃, 鹚, 旻, 饭, 掖, 浚,\n",
      "Nearest to 嶓: 鬖, 箬, 但, 皋, 衾, ④, 谳, 逶,\n",
      "Nearest to 愧: 軿, 敌, , 蹒, 黏, 矧, 傀, 凊,\n",
      "Average loss at step 332000: 0.546274\n",
      "Average loss at step 334000: 0.539209\n",
      "Average loss at step 336000: 0.538078\n",
      "Average loss at step 338000: 0.544556\n",
      "Average loss at step 340000: 0.541933\n",
      "Nearest to 堤: 窦, 璋, 谣, 瘥, 儡, 揭, 艺, 蛾,\n",
      "Nearest to 谨: 外, 代, 籧, 法, 捧, 嘘, 噪, 粱,\n",
      "Nearest to 谛: 翎, 屠, 萦, 儿, 拄, , 翡, 胡,\n",
      "Nearest to 渌: 鄘, 濠, 攥, 疾, 祝, 妹, 哩, 谠,\n",
      "Nearest to 筒: 疲, 萤, 纵, 到, 篰, 蔷, 哺, 焰,\n",
      "Nearest to 骍: 者, 习, 棣, 篰, 滪, 乇, 共, 帨,\n",
      "Nearest to 颣: 厩, 摩, 尖, 圈, 莠, 口, 醲, 滋,\n",
      "Nearest to 蜘: 簟, 蕾, 幕, 祲, 迸, 戊, 彳, 蹴,\n",
      "Nearest to 呕: 莫, 般, 甓, 僻, 囿, 店, 淄, 狮,\n",
      "Nearest to 泖: 邵, 灸, 诸, 奴, 蓬, 眇, 眄, 堍,\n",
      "Nearest to 邱: 陷, 嗷, 匣, 芰, 立, 飘, , 煜,\n",
      "Nearest to 贰: 躞, 趟, 挥, 不, 诫, 亩, 裉, 鵕,\n",
      "Nearest to 羊: 帽, 眚, 饱, 恻, 雏, 墓, 擢, 窗,\n",
      "Nearest to 牥: 侈, 纟, 瓃, 鹚, 浚, 旻, 掖, 饭,\n",
      "Nearest to 嶓: 鬖, 箬, 皋, 衾, 但, ④, 谳, 逶,\n",
      "Nearest to 愧: 軿, , 敌, 蹒, 黏, 矧, 傀, 凊,\n",
      "Average loss at step 342000: 0.546532\n",
      "Average loss at step 344000: 0.543879\n",
      "Average loss at step 346000: 0.540649\n",
      "Average loss at step 348000: 0.538531\n",
      "Average loss at step 350000: 0.542479\n",
      "Nearest to 堤: 窦, 璋, 谣, 瘥, 揭, 儡, 駣, 艺,\n",
      "Nearest to 谨: 外, 籧, 代, 法, 捧, 噪, 嘘, 榛,\n",
      "Nearest to 谛: 翎, 屠, 萦, 儿, 拄, , 胡, 翡,\n",
      "Nearest to 渌: 鄘, 攥, 疾, 濠, 哩, 妹, 抡, 逼,\n",
      "Nearest to 筒: 疲, 萤, 纵, 磅, 到, 篰, 蔷, 赪,\n",
      "Nearest to 骍: 者, 习, 滪, 篰, 乇, 棣, 共, 帨,\n",
      "Nearest to 颣: 厩, 摩, 莠, 圈, 尖, 醲, 口, 滋,\n",
      "Nearest to 蜘: 簟, 蕾, 幕, 祲, 迸, 戊, 蹴, 彳,\n",
      "Nearest to 呕: 莫, 般, 甓, 僻, 囿, 淄, 狮, 店,\n",
      "Nearest to 泖: 邵, 灸, 诸, 奴, 蓬, 眇, 狞, 堍,\n",
      "Nearest to 邱: 陷, 嗷, 立, 匣, 飘, 芰, , 序,\n",
      "Nearest to 贰: 躞, 趟, 诫, 挥, 不, 亩, 裉, 仟,\n",
      "Nearest to 羊: 帽, 雏, 眚, 饱, 恻, 墓, 擢, 窗,\n",
      "Nearest to 牥: 侈, 鹚, 纟, 瓃, 浚, 旻, 掖, 偿,\n",
      "Nearest to 嶓: 鬖, 但, 衾, 皋, 箬, 谳, ④, 郿,\n",
      "Nearest to 愧: 軿, , 蹒, 敌, 黏, 矧, 傀, 凊,\n",
      "Average loss at step 352000: 0.546571\n",
      "Average loss at step 354000: 0.542387\n",
      "Average loss at step 356000: 0.539422\n",
      "Average loss at step 358000: 0.546465\n",
      "Average loss at step 360000: 0.537585\n",
      "Nearest to 堤: 窦, 璋, 谣, 瘥, 揭, 儡, 駣, 艺,\n",
      "Nearest to 谨: 外, 代, 籧, 法, 捧, 噪, 嘘, 榛,\n",
      "Nearest to 谛: 翎, 萦, 屠, 拄, 儿, 翡, , 胡,\n",
      "Nearest to 渌: 鄘, 攥, 疾, 濠, 哩, 妹, 祝, 沦,\n",
      "Nearest to 筒: 疲, 萤, 纵, 到, 磅, 篰, 蔷, 焰,\n",
      "Nearest to 骍: 者, 习, 乇, 滪, 共, 篰, 棣, 聚,\n",
      "Nearest to 颣: 厩, 尖, 摩, 圈, 莠, 醲, 扰, 口,\n",
      "Nearest to 蜘: 簟, 蕾, 幕, 祲, 戊, 彳, 蹴, 咎,\n",
      "Nearest to 呕: 莫, 般, 甓, 僻, 囿, 狮, 淄, 攘,\n",
      "Nearest to 泖: 邵, 灸, 诸, 奴, 眇, 蓬, 狞, 堍,\n",
      "Nearest to 邱: 陷, 嗷, 匣, 立, 芰, 飘, , 序,\n",
      "Nearest to 贰: 躞, 趟, 挥, 诫, 不, 亩, 裉, 仟,\n",
      "Nearest to 羊: 帽, 饱, 眚, 雏, 恻, 墓, 擢, 洧,\n",
      "Nearest to 牥: 侈, 纟, 瓃, 鹚, 旻, 掖, 7, 浚,\n",
      "Nearest to 嶓: 鬖, 箬, 皋, 但, 衾, 谳, ④, 逶,\n",
      "Nearest to 愧: 軿, , 敌, 蹒, 黏, 矧, 傀, 凊,\n",
      "Average loss at step 362000: 0.543764\n",
      "Average loss at step 364000: 0.541784\n",
      "Average loss at step 366000: 0.547668\n",
      "Average loss at step 368000: 0.545651\n",
      "Average loss at step 370000: 0.533027\n",
      "Nearest to 堤: 窦, 璋, 揭, 儡, 谣, 瘥, ⒄, 艺,\n",
      "Nearest to 谨: 外, 代, 法, 籧, 捧, 噪, 榛, 嘘,\n",
      "Nearest to 谛: 翎, 萦, 屠, 拄, 儿, , 翡, 胡,\n",
      "Nearest to 渌: 鄘, 攥, 疾, 濠, 妹, 沦, 哩, 祝,\n",
      "Nearest to 筒: 疲, 萤, 纵, 磅, 到, 篰, 蔷, 樻,\n",
      "Nearest to 骍: 者, 习, 乇, 棣, 篰, 滪, 帨, 共,\n",
      "Nearest to 颣: 厩, 圈, 尖, 摩, 莠, 醲, 口, 滋,\n",
      "Nearest to 蜘: 簟, 蕾, 幕, 戊, 蹴, 彳, 祲, 迸,\n",
      "Nearest to 呕: 莫, 般, 甓, 僻, 囿, 秾, 攘, 淄,\n",
      "Nearest to 泖: 邵, 诸, 灸, 奴, 蓬, 眇, 堍, 狞,\n",
      "Nearest to 邱: 陷, 嗷, 芰, 匣, 立, 飘, 序, ,\n",
      "Nearest to 贰: 躞, 趟, 挥, 诫, 不, 亩, 裉, 仟,\n",
      "Nearest to 羊: 帽, 饱, 眚, 雏, 恻, 墓, 擢, 洧,\n",
      "Nearest to 牥: 侈, 纟, 瓃, 鹚, 浚, 旻, 掖, 艨,\n",
      "Nearest to 嶓: 鬖, 箬, 但, 皋, 衾, ④, 谳, 逶,\n",
      "Nearest to 愧: 軿, , 蹒, 敌, 黏, 傀, 矧, 远,\n",
      "Average loss at step 372000: 0.544044\n",
      "Average loss at step 374000: 0.546751\n",
      "Average loss at step 376000: 0.545148\n",
      "Average loss at step 378000: 0.543329\n",
      "Average loss at step 380000: 0.540277\n",
      "Nearest to 堤: 窦, 璋, 瘥, 谣, 儡, 揭, 苛, 艺,\n",
      "Nearest to 谨: 外, 代, 法, 籧, 捧, 噪, 嘘, 发,\n",
      "Nearest to 谛: 翎, 儿, 萦, 屠, 拄, , 缩, 瘥,\n",
      "Nearest to 渌: 鄘, 攥, 濠, 疾, 祝, 妹, 哩, 沦,\n",
      "Nearest to 筒: 疲, 萤, 纵, 到, 篰, 磅, 焰, 蔷,\n",
      "Nearest to 骍: 者, 习, 棣, 篰, 滪, 乇, 共, 帨,\n",
      "Nearest to 颣: 厩, 圈, 尖, 摩, 莠, 口, 滋, 醲,\n",
      "Nearest to 蜘: 簟, 蕾, 幕, 戊, 祲, 蹴, 彳, 咎,\n",
      "Nearest to 呕: 莫, 般, 甓, 僻, 囿, 淄, 攘, 狮,\n",
      "Nearest to 泖: 邵, 灸, 诸, 奴, 眇, 蓬, 眄, 狞,\n",
      "Nearest to 邱: 陷, 嗷, 芰, 匣, 序, 立, , 飘,\n",
      "Nearest to 贰: 躞, 趟, 挥, 诫, 不, 裉, 亩, 鵕,\n",
      "Nearest to 羊: 帽, 眚, 饱, 恻, 雏, 墓, 擢, 彩,\n",
      "Nearest to 牥: 侈, 纟, 鹚, 瓃, 旻, 掖, 艨, 7,\n",
      "Nearest to 嶓: 鬖, 皋, 衾, 箬, 但, 谳, ④, 逶,\n",
      "Nearest to 愧: 軿, , 敌, 蹒, 黏, 矧, 凊, 傀,\n",
      "Average loss at step 382000: 0.542441\n",
      "Average loss at step 384000: 0.538246\n",
      "Average loss at step 386000: 0.549761\n",
      "Average loss at step 388000: 0.539765\n",
      "Average loss at step 390000: 0.544227\n",
      "Nearest to 堤: 窦, 璋, 谣, 瘥, 揭, 儡, 艺, 駣,\n",
      "Nearest to 谨: 外, 籧, 捧, 代, 法, 噪, 嘘, 榛,\n",
      "Nearest to 谛: 翎, 萦, 屠, 拄, 儿, , 厌, 翡,\n",
      "Nearest to 渌: 鄘, 濠, 疾, 攥, 祝, 哩, 妹, 脂,\n",
      "Nearest to 筒: 疲, 纵, 萤, 到, 篰, 蔷, 哺, 焰,\n",
      "Nearest to 骍: 者, 习, 滪, 篰, 棣, 乇, 共, 帨,\n",
      "Nearest to 颣: 厩, 圈, 摩, 尖, 莠, 口, 醲, 颅,\n",
      "Nearest to 蜘: 簟, 蕾, 幕, 蹴, 祲, 戊, 迸, 彳,\n",
      "Nearest to 呕: 莫, 般, 甓, 僻, 囿, 淄, 狮, 秾,\n",
      "Nearest to 泖: 邵, 灸, 诸, 眇, 蓬, 奴, 眄, 窜,\n",
      "Nearest to 邱: 陷, 嗷, 匣, 芰, 立, 序, 飘, ,\n",
      "Nearest to 贰: 躞, 趟, 挥, 诫, 不, 仟, 鵕, 亩,\n",
      "Nearest to 羊: 帽, 眚, 饱, 恻, 雏, 墓, 擢, 洧,\n",
      "Nearest to 牥: 侈, 纟, 鹚, 瓃, 掖, 浚, 旻, 饭,\n",
      "Nearest to 嶓: 鬖, 皋, 箬, 衾, 但, ④, 谳, 逶,\n",
      "Nearest to 愧: 軿, , 蹒, 敌, 黏, 矧, 傀, 凊,\n",
      "Average loss at step 392000: 0.547062\n",
      "Average loss at step 394000: 0.537638\n",
      "Average loss at step 396000: 0.545596\n",
      "Average loss at step 398000: 0.540263\n",
      "Average loss at step 400000: 0.542275\n",
      "Nearest to 堤: 窦, 璋, 瘥, 揭, 艺, 谣, 儡, 駣,\n",
      "Nearest to 谨: 外, 代, 籧, 捧, 法, 噪, 嘘, 榛,\n",
      "Nearest to 谛: 翎, 萦, 屠, 拄, 儿, , 翡, 胡,\n",
      "Nearest to 渌: 鄘, 攥, 濠, 妹, 疾, 哩, 抡, 沦,\n",
      "Nearest to 筒: 疲, 萤, 纵, 篰, 到, 哺, 磅, 蔷,\n",
      "Nearest to 骍: 者, 滪, 习, 篰, 乇, 棣, 共, 帨,\n",
      "Nearest to 颣: 厩, 摩, 尖, 圈, 莠, 滋, 扰, 口,\n",
      "Nearest to 蜘: 簟, 蕾, 幕, 蹴, 祲, 迸, 戊, 彳,\n",
      "Nearest to 呕: 莫, 般, 甓, 僻, 囿, 淄, 狮, 秾,\n",
      "Nearest to 泖: 邵, 灸, 诸, 奴, 蓬, 眇, 堍, 眄,\n",
      "Nearest to 邱: 陷, 嗷, 匣, 立, 芰, , 飘, 序,\n",
      "Nearest to 贰: 躞, 趟, 挥, 诫, 不, 亩, 裉, 仟,\n",
      "Nearest to 羊: 帽, 眚, 饱, 雏, 恻, 墓, 擢, 虱,\n",
      "Nearest to 牥: 侈, 纟, 鹚, 旻, 瓃, 掖, 7, 浚,\n",
      "Nearest to 嶓: 鬖, 箬, 皋, 但, 衾, ④, 谳, 逶,\n",
      "Nearest to 愧: 軿, , 敌, 蹒, 黏, 矧, 傀, 凊,\n"
     ]
    }
   ],
   "source": [
    "num_steps = 400001\n",
    "from six.moves import xrange\n",
    "with tf.Session(graph=graph) as session:\n",
    "  tf.initialize_all_variables().run()\n",
    "  print('Initialized')\n",
    "  average_loss = 0\n",
    "  for step in range(num_steps):\n",
    "    batch_data, batch_labels = generate_batch(\n",
    "      batch_size, num_skips, skip_window)\n",
    "    feed_dict = {train_dataset : batch_data, train_labels : batch_labels}\n",
    "    _, l = session.run([optimizer, loss], feed_dict=feed_dict)\n",
    "    average_loss += l\n",
    "    if step % 2000 == 0:\n",
    "      if step > 0:\n",
    "        average_loss = average_loss / 2000\n",
    "      # The average loss is an estimate of the loss over the last 2000 batches.\n",
    "      print('Average loss at step %d: %f' % (step, average_loss))\n",
    "      average_loss = 0\n",
    "    # note that this is expensive (~20% slowdown if computed every 500 steps)\n",
    "    if step % 10000 == 0:\n",
    "      sim = similarity.eval()\n",
    "      for i in xrange(valid_size):\n",
    "        valid_word = reverse_dictionary[valid_examples[i]]\n",
    "        top_k = 8 # number of nearest neighbors\n",
    "        nearest = (-sim[i, :]).argsort()[1:top_k+1]\n",
    "        log = 'Nearest to %s:' % valid_word\n",
    "        for k in xrange(top_k):\n",
    "          close_word = reverse_dictionary[nearest[k]]\n",
    "          log = '%s %s,' % (log, close_word)\n",
    "        print(log)\n",
    "  final_embeddings = normalized_embeddings.eval()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "num_points = 400\n",
    "\n",
    "tsne = TSNE(perplexity=30, n_components=2, init='pca', n_iter=5000)\n",
    "two_d_embeddings = tsne.fit_transform(final_embeddings[1:num_points+1, :])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1afb6368e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.font_manager import FontProperties\n",
    "ChineseFont1 = FontProperties(fname = 'C:\\\\Windows\\\\Fonts\\\\simsun.ttc')\n",
    "\n",
    "def plot_with_labels(low_dim_embs, labels, filename):\n",
    "  assert low_dim_embs.shape[0] >= len(labels), 'More labels than embeddings'\n",
    "  plt.figure(figsize=(18, 18))  # in inches\n",
    "  for i, label in enumerate(labels):\n",
    "    x, y = low_dim_embs[i, :]\n",
    "    plt.scatter(x, y)\n",
    "    plt.annotate(label,\n",
    "                 xy=(x, y),\n",
    "                 xytext=(5, 2),\n",
    "                 textcoords='offset points',\n",
    "                 ha='right',fontproperties = ChineseFont1,\n",
    "                 va='bottom')\n",
    "\n",
    "  plt.savefig(filename)\n",
    "from matplotlib import font_manager\n",
    "\n",
    "try:\n",
    "  # pylint: disable=g-import-not-at-top\n",
    "  from sklearn.manifold import TSNE\n",
    "  import matplotlib.pyplot as plt\n",
    "\n",
    "  tsne = TSNE(perplexity=30, n_components=2, init='pca', n_iter=5000, method='exact')\n",
    "  plot_only = 500\n",
    "  low_dim_embs = tsne.fit_transform(final_embeddings[:plot_only, :])\n",
    "  labels = [reverse_dictionary[i] for i in xrange(plot_only)]\n",
    "  plot_with_labels(low_dim_embs, labels, os.path.join(gettempdir(), 'tsne.png'))\n",
    "except ImportError as ex:\n",
    "  print('Please install sklearn, matplotlib, and scipy to show embeddings.')\n",
    "  print(ex)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C:\\Users\\zhi\\AppData\\Local\\Temp\n"
     ]
    }
   ],
   "source": [
    "print(gettempdir())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tempfile import TemporaryFile\n",
    "outfile = TemporaryFile()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.save(outfile, final_embeddings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outfile.seek(0)dictionary, reverse_dictionary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.00121622,  0.00623623,  0.00786332, ..., -0.01806835,\n",
       "        -0.35388386,  0.0302667 ],\n",
       "       [-0.01271469,  0.02245513,  0.10717005, ..., -0.10162421,\n",
       "        -0.11170832,  0.10021088],\n",
       "       [-0.07742595, -0.06922957,  0.05039036, ...,  0.07292641,\n",
       "        -0.00301739,  0.04024274],\n",
       "       ..., \n",
       "       [-0.06806442,  0.07582098, -0.03712125, ...,  0.08247469,\n",
       "        -0.01650273, -0.08150265],\n",
       "       [-0.07800205,  0.08960694,  0.069544  , ...,  0.05628275,\n",
       "         0.02397816,  0.03784458],\n",
       "       [-0.05808216,  0.01478665, -0.04028169, ..., -0.03359636,\n",
       "        -0.04402011,  0.09237343]], dtype=float32)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.load(outfile)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "with open('dict.json','w') as fp:\n",
    "    json.dump(dictionary,fp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('revdict.json','w') as fp:\n",
    "    json.dump(reverse_dictionary,fp)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
